Jin Cao, Yuan Ge, Dongdong Wang, Qiyou Lin, Renfeng Chen
{"title":"基于加权复杂网络的电动汽车充电指导","authors":"Jin Cao, Yuan Ge, Dongdong Wang, Qiyou Lin, Renfeng Chen","doi":"10.1080/21642583.2022.2135632","DOIUrl":null,"url":null,"abstract":"The charging station malfunction may deny the charging service for the electric vehicles at the station. As a result, the vehicles need to select other stations. How to make an optimal selection is difficult for the owners. An optimal charging guidance strategy based on a weighted complex network is proposed for the owners to select the optimal station. All the charging stations are modelled as a complex network in which the stations and the roads among them are defined as nodes and edges, respectively. Furthermore, each edge is weighted by the state of charge (SOC) of the vehicle, the charging price, and the distance and traffic conditions between these two stations. The bigger edge weight indicates the smaller probability that the owner at one node of the edge select the other node of the edge for charging, and vice versa. Based on the weighted complex network model, the local load redistribution method is presented to guide the charging of vehicles at the malfunctioning station. Consequently, the optimal scheduling of the vehicles is realized under the guidance strategy proposed in this paper. Finally, some contrast experiments are carried out to illustrate the effectiveness and the superiority of the proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Electric vehicle charging guidance based on weighted complex network\",\"authors\":\"Jin Cao, Yuan Ge, Dongdong Wang, Qiyou Lin, Renfeng Chen\",\"doi\":\"10.1080/21642583.2022.2135632\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The charging station malfunction may deny the charging service for the electric vehicles at the station. As a result, the vehicles need to select other stations. How to make an optimal selection is difficult for the owners. An optimal charging guidance strategy based on a weighted complex network is proposed for the owners to select the optimal station. All the charging stations are modelled as a complex network in which the stations and the roads among them are defined as nodes and edges, respectively. Furthermore, each edge is weighted by the state of charge (SOC) of the vehicle, the charging price, and the distance and traffic conditions between these two stations. The bigger edge weight indicates the smaller probability that the owner at one node of the edge select the other node of the edge for charging, and vice versa. Based on the weighted complex network model, the local load redistribution method is presented to guide the charging of vehicles at the malfunctioning station. Consequently, the optimal scheduling of the vehicles is realized under the guidance strategy proposed in this paper. Finally, some contrast experiments are carried out to illustrate the effectiveness and the superiority of the proposed method.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2022.2135632\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2135632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Electric vehicle charging guidance based on weighted complex network
The charging station malfunction may deny the charging service for the electric vehicles at the station. As a result, the vehicles need to select other stations. How to make an optimal selection is difficult for the owners. An optimal charging guidance strategy based on a weighted complex network is proposed for the owners to select the optimal station. All the charging stations are modelled as a complex network in which the stations and the roads among them are defined as nodes and edges, respectively. Furthermore, each edge is weighted by the state of charge (SOC) of the vehicle, the charging price, and the distance and traffic conditions between these two stations. The bigger edge weight indicates the smaller probability that the owner at one node of the edge select the other node of the edge for charging, and vice versa. Based on the weighted complex network model, the local load redistribution method is presented to guide the charging of vehicles at the malfunctioning station. Consequently, the optimal scheduling of the vehicles is realized under the guidance strategy proposed in this paper. Finally, some contrast experiments are carried out to illustrate the effectiveness and the superiority of the proposed method.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory